MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)
C
This setup is broadly balanced for this model.82.6 tok/s Dekodierung
ca. $2,499 MSRP
Can Aya Expanse 8B run on RTX 5000 Ada 32GB?
YES — Runs Great
C51Usable
Estimated from fit model
Aya Expanse 8B needs ~10.9 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~102 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 10.9 GB, 101.5 tok/s, Runs well
10.9 GB required32.0 GB available
34% VRAM used
Fit status
Runs well
Decode
101.5 tok/s
TTFT
1907 ms
Safe context
8K
Memory
10.9 GB / 32.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsAya Expanse 8B on RTX 5000 Ada 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 101.5 tok/s decode · 1.9s TTFT (warm) · 254 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 101.5 tok/s | 1040 ms | 8K |
| Coding | C | Runs well | 101.5 tok/s | 1907 ms | 8K |
| Agentic Coding | C | Runs well | 101.5 tok/s | 2774 ms | 8K |
| Reasoning | C | Runs well | 101.5 tok/s | 2254 ms | 8K |
| RAG | C | Runs well | 101.5 tok/s | 3468 ms | 8K |
Quantization options
How Aya Expanse 8B (8B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 | 4.5 GB | Medium | C45 |
Q4_K_M | 4 | 4.9 GB | Medium | C45 |
Q5_K_M | 5 | 5.8 GB | High | C46 |
Q6_K | 6 | 6.6 GB | High | C46 |
Q8_0 | 8 | 8.6 GB | Very High | C47 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C51 |
Get started
Copy-paste commands to run Aya Expanse 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "CohereForAI/aya-expanse-8b" \
--hf-file "aya-expanse-8b-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
Hardware, die Aya Expanse 8B gut ausführt
Frequently asked questions
Can RTX 5000 Ada 32GB run Aya Expanse 8B?
Yes, RTX 5000 Ada 32GB can run Aya Expanse 8B with a C grade (Runs well). Expected decode speed: 101.5 tok/s.
How much VRAM does Aya Expanse 8B need?
Aya Expanse 8B (8B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Aya Expanse 8B?
The recommended quantization for Aya Expanse 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Aya Expanse 8B run at on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, Aya Expanse 8B achieves approximately 101.5 tokens per second decode speed with a time-to-first-token of 1907ms using Q4_K_M quantization.
Can RTX 5000 Ada 32GB run Aya Expanse 8B for coding?
For coding workloads, Aya Expanse 8B on RTX 5000 Ada 32GB receives a C grade with 101.5 tok/s and 8K context.
What context window can Aya Expanse 8B use on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, Aya Expanse 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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